Publication Details

A Call for Knowledge-based Planning

by Wilkins, D. E. and desJardins, M.

AI Magazine, vol. 22, no. 1, pp. 99-115, Spring 2001.

Abstract

We are interested in solving real-world planning problems and, to that end,
argue for the use of domain knowledge in planning. We believe that the field
must develop methods capable of using rich knowledge models in order to make
planning tools useful for complex problems.
We discuss the suitability of current planning paradigms for solving these
problems. In particular, we compare knowledge-rich approaches such as
hierarchical task network (HTN) planning to minimal-knowledge methods such as
STRIPS-based planners and disjunctive planners (DPs). We argue that the
former methods have advantages such as scalability, expressiveness,
continuous plan modification during execution, and the ability to interact
with humans. However, these planners also have limitations, such as
requiring complete domain models and failing to model uncertainty, that often
make them inadequate for real-world problems.
In this paper, we define the terms knowledge-based (KB) and
primitive-action (PA) planning, and argue for the use of KB planning
as a paradigm for solving real-world problems. We next summarize some
of the characteristics of real-world problems that we are interested
in addressing. Several current real-world planning applications
are described, focusing on the ways in which knowledge is brought to bear on
the planning problem.
We describe some existing KB approaches, and then discuss additional
capabilities, beyond those available in existing systems, that are needed.
Finally, we draw an analogy from the current
focus of the planning community on disjunctive planners to the experiences of the machine learning community over the past decade.